Bibliographic record
Abstract
This data was collected as part of the AKSSF funding #52011 awarded to the Alaska Department of Fish and Game, Sportfish division in Region III. Chinook salmon are an important subsistence resource throughout the Yukon River drainage and the Chena River supports one of the largest spawning populations in the Alaska portion. This project will estimate Chinook and chum salmon by visually counting fish as they pass over a continuous band of white fabric panels strung across the river bottom on the upstream side of the Moose Creek Dam. A DIDSON sonar unit and an ARIS sonar unit will be deployed upstream of the tower site on both sides of the river to ensonify the river at all times and record the number of migrating salmon throughout the run. A mixture model will be used to estimate Chinook and chum salmon during extended periods of high water when tower counts cannot be completed. In addition to enumeration, carcasses of spawned-out Chinook and chum salmon will be collected during the last week of July through the first two weeks of August to estimate the ASL composition of the escapement. The citation for the operational plan for this project is below: Matter, A. N., and M. Tyers. 2019. Chinook salmon escapement in the Chena and Salcha Rivers and Coho salmon escapement in the Delta Clearwater River, 2019-2023. Alaska Department of Fish and Game, Regional Operational Plan ROP.SF.3F.2019.03, Anchorage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".